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app.py
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app.py
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#importing Dependencies
import streamlit as st
import pickle
import pandas as pd
import requests
#loading required files from colab
movies_list=pickle.load(open("movies.pkl","rb"))
similarity=pickle.load(open("similarity.pkl","rb"))
movies_dict=pickle.load(open("moviesdict.pkl","rb"))
movies=pd.DataFrame(movies_dict)
#function to fetch the poster of recommended movie
def fetch_poster(movie_id):
url = "https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US".format(movie_id)
data = requests.get(url)
data = data.json()
poster_path = data['poster_path']
full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
return full_path
#function to fetch the recommended movies
def recommend(movie):
index = movies[movies['title'] == movie].index[0]
distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1])
recommended_movies=[]
recommended_movie_posters=[]
for i in distances[1:6]:
movie_id = movies.iloc[i[0]].id
recommended_movie_posters.append(fetch_poster(movie_id))
recommended_movies.append(movies.iloc[i[0]].title)
return recommended_movies,recommended_movie_posters
#streamlit app
st.title("Movie Recommender")
selected_movie_name = st.selectbox(
'Select Your Favorite movie',movies_list)
st.write('You selected:', selected_movie_name)
if st.button('Show Recommendation'):
recommended_movies,recommended_movie_posters = recommend(selected_movie_name)
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.text(recommended_movies[0])
st.image(recommended_movie_posters[0])
with col2:
st.text(recommended_movies[1])
st.image(recommended_movie_posters[1])
with col3:
st.text(recommended_movies[2])
st.image(recommended_movie_posters[2])
with col4:
st.text(recommended_movies[3])
st.image(recommended_movie_posters[3])
with col5:
st.text(recommended_movies[4])
st.image(recommended_movie_posters[4])